Key Responsibilities
Assemble architecture designs produce clear, end-to-end architecture artifacts: solution designs, reference architectures, diagrams, patterns, and architecture decision records.
Apply AI capabilities design and integrate AI / GenAI / machine-learning components into solutions, and advise on appropriate patterns, tooling, and trade-offs.
Drive rapid prototypes build quick proofs of concept and working prototypes to validate architecture decisions, de-risk options, and accelerate stakeholder alignment.
Partner across teams work with engineering, product, and business partners to align architecture with delivery goals and non-functional requirements (security, scalability, resilience, cost).
Document and communicate present designs and trade-offs clearly to both technical and non-technical audiences, and keep architecture documentation current and usable.
Uphold standards apply enterprise architecture standards, controls, and best practices throughout design and prototyping.
Required Qualifications (Must-Have)
Demonstrable hands-on AI experience practical work building, integrating, or architecting AI / GenAI / ML solutions.
Proven ability to put architecture details together a track record of producing clear, complete architecture designs and documentation.
Ability to drive quick prototypes rapidly stand up POCs and working prototypes to test and demonstrate ideas.
Senior, hands-on technical background operating at a Lead Architect level, with strong design fundamentals across modern application, integration, and cloud patterns.
Strong communication skills able to explain architecture and trade-offs to technical teams and business stakeholders alike.
Self-directed and comfortable working across ambiguity to deliver tangible outcomes quickly.
Technical Skills
Representative technical skills for these roles. Candidates should bring strong depth across several of these areas tailor to our stack as needed:
AI & Machine Learning GenAI and large language models (LLMs), retrieval-augmented generation (RAG), agentic and prompt-engineering patterns, model APIs and integration, embeddings and vector stores; familiarity with common ML frameworks.
Cloud & Platform hands-on experience with at least one major cloud (AWS, Azure, or Google Cloud Platform); containers and orchestration (Docker, Kubernetes); serverless services.
Architecture & Integration microservices, event-driven and API-led design, REST / GraphQL APIs, messaging and streaming (e.g., Kafka), and enterprise integration patterns.
Data & Information Architecture data modeling, relational and NoSQL databases (strong SQL), data lakes / warehouses, ETL / ELT pipelines, and data governance / metadata.
Languages & Prototyping proficiency in Python and/or Java (or comparable); rapid prototyping, scripting, and notebook-based experimentation.
Engineering Practices CI/CD, infrastructure as code (e.g., Terraform), Git-based version control, and automated testing.
Architecture Tooling modeling and diagramming (C4, UML, or ArchiMate) and architecture decision records (ADRs).
Preferred Qualifications (Nice-to-Have)
The following are strong pluses and will differentiate candidates, but are not strict requirements:
Depth in data / information architecture experience designing data models, information flows, data platforms, or enterprise information architecture.
Finance domain experience prior work in Finance functions, especially Controllers / financial control and related processes.
What Success Looks Like
Architecture artifacts and prototypes are delivered quickly and are clear enough to drive decisions.
AI options are evaluated and applied pragmatically, with sound trade-off analysis.
Stakeholders trust the designs and can act on them with minimal rework.
Notes for Sourcing
Please share this description with vendor partners to identify candidates who meet the must-have criteria first, with preference given to those who also bring the data/information architecture and Finance (Controllers) experience noted above. Placeholders in the Role Snapshot (duration, start date, location, and rate band) should be completed before distribution.
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